MRI has enormous potential for dynamic imaging of various diseases and physiological processes, which has not been fully utilized for clinical applications due to the limited imaging speed of existing technology. The quest for higher imaging speeds has been a major driving force for MRI research since its invention. Although tremendous progress has been made in fast MRI technology over the last three decades, virtually all MRI applications could benefit from additional speedups, and many potential applications would become possible only with significant acceleration. The primary objective of the proposed project is to produce significantly faster MRI technology by leveraging the recent breakthroughs in sparse sampling theory and the novel work of the PI s group in this area. This objective will be achieved with specific research efforts on: a) developing and optimizing a novel method for image reconstruction from highly undersampled (k, t)-space data using both partial separability and spatial-spectral constraints, b) analyzing and characterizing the resolution and noise properties of the proposed methods, and c) evaluating and validating the proposed method for cardiac imaging applications using phantom and rat studies. The outcome of the research effort will be significant in several ways. First, it will provide a new mathematical and algorithmic framework that effectively exploits the sparsity and partial separability of multidimensional MRI signals; this framework will enable sparse data sampling and significantly accelerate current MRI methods. Second, it will produce new MR imaging technology that will enhance the performance of existing MRI systems and provide a new way to optimize the design of MR data acquisition and image reconstruction in current and next-generation MRI systems. Third, it will enable a range of challenging dynamic imaging experiments, including realtime 3D cardiac imaging applications (e.g., functional assessment of transplanted hearts).